Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts

This work demonstrates that Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals that arise from corpus construction, case distribution, and confounding factors. To mitigate this, we use domain expertise to strategically identify statistically predictive but legally irrelevant information. We adopt adversarial training to prevent the system from relying on it. We evaluate our deconfounded models by employing interpretability techniques and comparing to expert annotations. Quantitative experiments and qualitative analysis show that our deconfounded model consistently aligns better with expert rationales than baselines trained for prediction only. We further contribute a set of reference expert annotations to the validation and testing partitions of an existing benchmark dataset of European Court of Human Rights cases. * Our rationales and code are available at https://github.com/TUMLegalTech/deconfounding_echr_ emnlp22 * The LexGLUE dataset does not contain metadata (case id, Respondent state etc); in this work we use an enriched version of the same dataset by Mathurin Aché. * The annotation explanations in (Chalkidis et al., 2021) state that "The annotator selects the factual paragraphs that "clearly" indicate allegations for the selected article(s)". We hypothesize that the so annotated passages contain information that is legally relevant for the violation as well.